motief/thoughts/ledgers/CONTINUITY_stemwijzer.md

Session: stemwijzer

Updated: 2026-03-23T09:00:00Z

Goal

2D political compass + motion similarity search from parliamentary votes + motion text. Full historical coverage 2016–2026, precomputed similarity cache, fused (SVD + text) embeddings.

Constraints

• DuckDB only (data/motions.db); open/close duckdb.connect(self.db_path) per method
• Vectors stored as JSON text (no external vector DB)
• Logging via logging.getLogger(__name__); no print() in library modules
• Tests run offline (network monkeypatched) — use .venv/bin/python -m pytest -q
• Do NOT modify app.py or scheduler.py
• Use .venv/bin/python (Arch Linux system Python is externally managed)

Current DB State (verified 2026-03-22 ~16:00)

Table	Rows
motions	10,613
embeddings	10,753
svd_vectors	24,528
fused_embeddings	10,613 (1:1 with motions, 0 duplicates)
similarity_cache	212,206 (top_k=20, all annual windows)
mp_votes	199,967
mp_metadata	798

Annual Window Coverage

Year	Motions	Fused	Similarity
2016	132	132	2,640
2017	30	30	600
2018	100	100	2,000
2019	3	3	6
2020	0	0	0 (no data)
2021	0	0	0 (no data)
2022	4,116	4,116	82,320
2023	621	621	12,420
2024	948	948	18,960
2025	3,715	3,715	74,300
2026	948	948	18,960

Completed This Session

• Text embeddings: ran with real OpenRouter API at batch_size=200 → 10,753 embedding rows
• Re-ran extract_mp_votes on all motions → 111,978 new rows (party-level votes backfilled)
• SVD re-run (annual 2016–2026) with full vote data → 24,528 svd_vector rows
• Fixed store_fused_embedding double-counting bug: added DELETE before INSERT
• Cleaned and re-ran fusion → 10,613 fused rows, zero duplicates
• Re-ran similarity cache top_k=20 for all 9 active windows → 212,206 rows
• Test suite: 34 passed, 2 skipped ✅

Key Decisions

• store_fused_embedding (database.py line 686): Now does DELETE+INSERT instead of plain INSERT to prevent duplicates on re-runs.
• Annual windows chosen for historical political compass (2016–2026).
• top_k=20 for similarity cache.
• Party-level votes (e.g. {"PVV": "voor"}) handled in extract_mp_votes — actor without comma → party=actor_name.

Open Items (not blocking, data coverage gaps)

1. 2020–2021 data gap: No motions in DB at all. Need to run downloader with --start-date 2019-01-01 --end-date 2021-12-31 if data exists in API.
2. 2024 gap ~3,020 motions: OData API has ~3,968 2024 motions, only 948 in DB. Root cause unclear — needs investigation of URL-based dedup in insert_motion.
3. "Verworpen." dedup: Short-text motions (title="Verworpen.") get spurious similarity=1.0. UI/query layer should filter score < 0.999 OR title != 'Verworpen.'.
4. svd_vectors has duplicates: 2025 has 7,430 rows for 3,715 motions (2x). Doesn't affect fused_embeddings (DELETE+INSERT handles it) but wastes space. Low priority.

Key File Paths

• DB: data/motions.db
• Venv: .venv/bin/python
• Pipeline entry: pipeline/run_pipeline.py
• Fusion: pipeline/fusion.py
• SVD: pipeline/svd_pipeline.py
• Text embeddings: pipeline/text_pipeline.py
• MP votes extraction: pipeline/extract_mp_votes.py
• Database layer: database.py
• Similarity compute: similarity/compute.py
• Similarity lookup: similarity/lookup.py
• Tests: tests/ (pytest, offline)

Branch

main

Progress

Done

• All items listed under "Completed This Session" above

In Progress

• Rerun embeddings: started scripts/rerun_embeddings.py against data/motions.db
  • Start time: 2026-03-23T01:42:00Z (approx)
  • Current progress: embeddings stored = 950 / total motions = 28,172
  • fused_embeddings = 0 (not started)
  • similarity_cache = 0 (not started)

Blocked

• Not fully blocked, but encountering provider failures and warnings that slow progress:
  • Batch 951..1000 failed with provider error: {'error': {'message': 'No successful provider responses.', 'code': 404}} (recorded)
  • Occasional connection pool warnings during earlier body fetch phase (logged)
  • Provider failures are transient but may require retries or provider change if repeated

Key Decisions

• Retry strategy on provider failure: On repeated provider failures, retry embedding batches with smaller batch_size (e.g. 50 -> 20) or switch provider. Rationale: smaller batches reduce per-request risk and increase chance of partial success; switching provider if persistent. (UNCONFIRMED)

Next Steps

1. Continue the rerun_embeddings job until completion; monitor batches closely
2. If provider failures repeat, retry failed batches with smaller batch_size (50 -> 20) or switch provider (as above)
3. On completion, update ledger with final counts and list any failed motion IDs
4. If fused_embeddings / similarity_cache remain 0 after embeddings finished, run fusion and similarity recompute pipelines

File Operations

Read

• data/motions.db
• scripts/rerun_embeddings.py (invoked)

Modified

• thoughts/ledgers/CONTINUITY_stemwijzer.md (this file)

Critical Context

• Rerun started 2026-03-23T01:42Z; current embeddings stored = 950 of 28,172 total motions.
• Recent error: Batch 951..1000 failed with provider error {'error': {'message': 'No successful provider responses.', 'code': 404}} — these batch numbers and error payload should be retried.
• ETA: approx 1.5–2.5 hours remaining at current rate (UNCONFIRMED, depends on provider stability)
• Earlier stage produced occasional connection pool warnings while fetching motion bodies; these did not stop progress but may indicate transient network instability.

Working Set

• Branch: main
• Key files: data/motions.db, scripts/rerun_embeddings.py, thoughts/ledgers/CONTINUITY_stemwijzer.md